We introduce Ghostbuster, a state-of-the-art system for detecting
AI-generated text. Our method works by passing documents through a series of
weaker language models and running a structured search over possible
combinations of their features, then training a classifier on the selected
features to determine if the target document was AI-generated. Crucially,
Ghostbuster does not require access to token probabilities from the target
model, making it useful for detecting text generated by black-box models or
unknown model versions. In conjunction with our model, we release three new
datasets of human and AI-generated text as detection benchmarks that cover
multiple domains (student essays, creative fiction, and news) and task setups:
document-level detection, author identification, and a challenge task of
paragraph-level detection. Ghostbuster averages 99.1 F1 across all three
datasets on document-level detection, outperforming previous approaches such as
GPTZero and DetectGPT by up to 32.7 F1.

Ghostbuster 是一种用于检测人工智能生成文本的最先进系统，它不需要访问目标模型中的令牌概率，可以用于检测黑盒模型或未知模型版本的文本。与我们的模型结合使用，我们发布了三个新的以人类和 AI 生成的文本作为检测基准的数据集，Ghostbuster 在全三个数据集上的文件级别检测平均达到了 99.1 F1，跑赢了 GPTZero 和 DetectGPT 等先前的方法。